Approaches to Detect PARP-1 Activation In Vivo, In Situ, and In Vitro
Bibliographic record
Abstract
An accurate and sensitive detection of catalytic activation of poly(ADP-ribose) polymerase-1 (PARP-1) is required to be performed in a wide variety of samples because this activity plays a role in various cellular responses to DNA damage ranging from DNA repair to cell death, as well as in housekeeping functions, such as transcription. Since PARP-1 gene is expressed constitutively, its activation cannot be surmised from increased expression of its mRNA or protein, but by demonstrating the consequences of its catalytic -reaction which results in consumption of the substrate nicotinamide adenine dinucleotide (NAD(+)) and formation of three products, namely, polymer of ADP-ribose (pADPr or PAR), nicotinamide, and protons. Here, we describe various approaches commonly used in our laboratory for detection of PARP-1 activation in vivo (cells, tissues, and tumors), in situ, and in vitro via assessment of formation of pADPr, depletion of the substrate NAD, or formation of protons resulting in rapid and reversible intracellular acidification. It is important to note that although some other members of the PARP family can carry out the same catalytic reaction, many of these assays largely reflect PARP-1 activation in a vast majority of the experimental circumstances and more specifically in DNA damage responses. However, if required, PARP-1-specific action should be confirmed by use of PARP-1 knockout or RNAi-mediated knockdown approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".